Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean (HICO) sensor

Keith, Darryl J.; Schaeffer, Blake A.; Lunetta, Ross S.; Gould, Richard W.; Rocha, Kenneth; Cobb, Donald

doi:10.1080/01431161.2014.894663

Cited by 73 publications

(37 citation statements)

References 56 publications

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“…The Hyperspectral Imager for the Coastal Ocean (HICO) is the first hyperspectral sensor designed specifically for the coastal ocean and estuarial, riverine, or other shallow-water areas with optimized Signal-to-Noise Ratio (SNR) [21]. It has been successfully applied for the study of phytoplankton, colored dissolved organic matter (CDOM), turbidity, and bathymetry in coastal waters [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Developing a Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana

2016

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Abstract:A comprehensive spectral-biogeochemical database was developed for the Wabash River and the Tippecanoe River in Indiana, United States. This database includes spectral measurements of river water, coincident in situ measurements of water quality parameters (chlorophyll (chl), non-algal particles (NAP), and colored dissolved organic matter (CDOM)), nutrients (total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC)), water-column inherent optical properties (IOPs), water depths, substrate types, and bottom reflectance spectra collected in summer 2014. With this dataset, the temporal variability of water quality observations was first analyzed and studied. Second, radiative transfer models were inverted to retrieve water quality parameters using a look-up table (LUT) based spectrum matching methodology. Results found that the temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions. Meanwhile, there were no significant correlations found between these parameters and streamflow for the Tippecanoe River, due to the two upstream reservoirs, which increase the settling of sediment and uptake of nutrients. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflow (CSO)), water temperature, and nutrients were important factors controlling instream concentrations of phytoplankton. The LUT retrieved NAP concentrations were in good agreement with field measurements with slope close to 1.0 and the average estimation error was 4.1% of independently obtained lab measurements. The error for chl estimation was larger (37.7%), which is attributed to the fact that the specific absorption spectrum of chl was not well represented in this study. The LUT retrievals for CDOM experienced large variability, probably due to the small data range collected in this study and the insensitivity of R rs to CDOM change. It is concluded that the success of the LUT method requires accurate spectral measurements and enough a priori information of the environment to construct a representative database for water quality retrieval. Therefore, future work will focus on continuing data collection in other seasons of the year and better characterization of the study area.

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Section: Introductionmentioning

confidence: 99%

Developing a Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana

2016

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“…In NR, R rs data were retrieved at stations along the salinity gradient also using a Satlantic HyperPro remote sensing system which logged in-water radiance, sky radiance and downwelling irradiance from 350 to 800 nm ( Figure 1b; Sokoletsky et al [38]). For the Florida estuaries, R rs was retrieved at stations using the Satlantic HyperSAS system which logged above-water radiance, sky radiance and downwelling irradiance from 350 to 800 nm (Keith et al [39]. The above-water R rs spectra were corrected following procedures in Gould et al [40].…”

Section: Methodsmentioning

confidence: 99%

“…Spectra were normalized for backscattering of small particles and colloids using Pegau et al [44]. Salinity was concurrently measured at each station using a Seabird 25 SBE CTD system (Keith et al [39]). …”

Section: Determination Of Cdom Absorption Coefficientsmentioning

confidence: 99%

Optical Models for Remote Sensing of Colored Dissolved Organic Matter Absorption and Salinity in New England, Middle Atlantic and Gulf Coast Estuaries USA

2016

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Ocean color algorithms have been successfully developed to estimate chlorophyll a and total suspended solids concentrations in coastal and estuarine waters but few have been created to estimate light absorption due to colored dissolved inorganic matter (CDOM) and salinity from the spectral signatures of these waters. In this study, we used remotely sensed reflectances in the red and blue-green portions of the visible spectrum retrieved from Medium Resolution Imaging Spectrometer (MERIS) and the International Space Station (ISS) Hyperspectral Imager for the Coastal Ocean (HICO) images to create a model to estimate CDOM absorption. CDOM absorption results were then used to develop an algorithm to predict the surface salinities of coastal bays and estuaries in New England, Middle Atlantic, and Gulf of Mexico regions. Algorithm-derived CDOM absorptions and salinities were successfully validated using laboratory measured absorption values over magnitudes of~0.1 to 7.0 m´1 and field collected CTD data from oligohaline to polyhaline (S less than 5 to 18-30) environments in Narragansett Bay (Rhode Island); the Neuse River Estuary (North Carolina); Pensacola Bay (Florida); Choctawhatchee Bay (Florida); St. Andrews Bay (Florida); St. Joseph Bay (Florida); and inner continental shelf waters of the Gulf of Mexico.

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“…For each survey station, each of the above-water spectrum measurements were performed with a co-coinciding water sample. Most of the measurements were taken by strictly following the Specifications for Oceanographic Survey-Marine Geology and Geophysics Investigation (GB/T 13909-92) and NASA SIMBIOS ocean optic protocols [18][19].…”

Section: Measuring and Processing Samples And Datamentioning

confidence: 99%

Seasonal Distribution of Suspended Particulate Matter off China’s Subei Coast

Song¹,

Li²,

Meng³

et al. 2018

Pol. J. Environ. Stud.

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Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean (HICO) sensor

Cited by 73 publications

References 56 publications

Developing a Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana

Developing a Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana

Optical Models for Remote Sensing of Colored Dissolved Organic Matter Absorption and Salinity in New England, Middle Atlantic and Gulf Coast Estuaries USA

Seasonal Distribution of Suspended Particulate Matter off China’s Subei Coast

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